Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Random Forest× | Albero decisionale× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2001 | 1984 |
| Ideatore≠ | Breiman, L. | Breiman, Friedman, Olshen & Stone |
| Tipo≠ | Ensemble (bagging of decision trees) | Recursive partitioning (if-then rules) |
| Fonte seminale≠ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Alias≠ | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. |
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